@article {Mesquita3486, author = {Rafael Mesquita and Gabriele Spina and Fabio Pitta and David Donaire-Gonzalez and Brenda M. Deering and Mehul S. Patel and Katy E. Mitchell and Jennifer Alison and Arnoldus J. R. van Gestel and Stefanie Zogg and Philippe Gagnon and Beatriz Abascal-Bolado and Barbara Vagaggini and Judith Garcia-Aymerich and Sue C. Jenkins and Elisabeth A. P. M. Romme and Samantha S.C. Kon and Paul S. Albert and Benjamin Waschki and Dinesh Shrikrishna and Sally J. Singh and Nicholas S. Hopkinson and David Miedinger and Roberto P. Benzo and Fran{\c c}ois Maltais and Pierluigi Paggiaro and Zoe J. McKeough and Michael I. Polkey and Kylie Hill and William D-C. Man and Christian F. Clarenbach and Nidia A. Hernandes and Daniela Savi and Sally Wootton and Karina C. Furlanetto and Li W. Cindy Ng and Anouk W. Vaes and Christine Jenkins and Peter R. Eastwood and Diana Jarreta and Anne Kirsten and Dina Brooks and David R. Hillman and Tha{\'\i}s Sant{\textquoteright}Anna and Kenneth Meijer and Selina D{\"u}rr and Malcolm Kohler and Vanessa S. Probst and Ruth Tal-Singer and Esther Garcia Gil and J{\"o}rg D. Leuppi and Peter M.A. Calverley and Frank W. J. M. Smeenk and Richard W. Costello and Marco Gramm and Roger Goldstein and Miriam Groenen and Helgo Magnussen and Emiel F.M. Wouters and Richard L. ZuWallack and Oliver Amft and Henrik Watz and Martijn A. Spruit}, title = {Late-breaking abstract: Cluster analysis of objectively measured physical activity in 1001 COPD patients}, volume = {44}, number = {Suppl 58}, elocation-id = {3486}, year = {2014}, publisher = {European Respiratory Society}, abstract = {Background: Detailed analyses of physical activity (PA) measures in chronic obstructive pulmonary disease (COPD) have been insufficiently explored. We aimed to identify clusters of COPD patients based on objectively measured PA data, and to compare clinical characteristics, PA measures and PA hourly patterns between these clusters.Methods: 1001 COPD patients (65\% men; median age and FEV1: 67 yrs and 49\%pred, respectively) from 10 countries were studied. PA measures and hourly patterns were analysed based on data from the multi-sensor Sensewear armband used for \>=4 days. Principal component analysis was applied to PA data for dimensionality reduction, subsequently k-means cluster analysis was used to identify subgroups of COPD patients.Results: 5 clusters were identified (Table 1).Cluster 1 (very inactive) spent less time in moderate-to-vigorous intensity and more time in very light intensity, whilst cluster 5 (very active) presented an opposite behaviour. Cluster 1 also presented higher body mass index, lower FEV1 and worse dyspnoea compared to other clusters. PA hourly patterns revealed that in all clusters the peak of intensity occurred before midday, but also that more inactive clusters had a more similar pattern between week and weekend (Figure 1).Conclusions: Five subgroups of COPD patients were identified with distinct PA measures and hourly patterns. These findings may serve as a basis for tailored interventions in COPD.}, issn = {0903-1936}, URL = {https://erj.ersjournals.com/content/44/Suppl_58/3486}, eprint = {https://erj.ersjournals.com/content}, journal = {European Respiratory Journal} }